scholarly journals Basis profile curve identification to understand electrical stimulation effects in human brain networks

2021 ◽  
Vol 17 (9) ◽  
pp. e1008710
Author(s):  
Kai J. Miller ◽  
Klaus-Robert Müller ◽  
Dora Hermes
Keyword(s):  
Brain Networks ◽  
Single Pulse ◽  
Electrical Current ◽  
Human Patient ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Brain Site ◽  
Data Driven Approach ◽  
Straightforward Interpretation ◽  
Natural Way

Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.

scholarly journals Basis profile curve identification to understand electrical stimulation effects in human brain networks

2021 ◽  
Author(s):  
Kai J. Miller ◽  
Klaus-Robert Müller ◽  
Dora Hermes
Keyword(s):  
Electrical Stimulation ◽  
Brain Area ◽  
Brain Networks ◽  
Single Pulse ◽  
Electrical Current ◽  
Brain Regions ◽  
Parahippocampal Gyrus ◽  
Illustrative Case ◽  
Human Patient ◽  
Brain Surface

AbstractBrain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.Author summaryWe present a new machine learning framework to probe how brain regions interact using single-pulse electrical stimulation. Unlike previous studies, this approach does not assume a form for how one brain area will respond to stimulation in another area, but rather discovers the shape of the response in time from the data. We call the set of characteristic discovered response shapes “basis profile curves” (BPCs), and show how these can be mapped back onto the brain quantitatively. An illustrative example is included from one of our human patients to characterize inputs to the parahippocampal gyrus. A code package is downloadable from https://purl.stanford.edu/rc201dv0636 so the reader may explore the technique for own data, or study sample data provided to reproduce the illustrative case presented in the manuscript.

Brain-Machine Interface Using Brain Surface Electrodes

2013 ◽  
pp. 1535-1548
Author(s):  
Masayuki Hirata ◽  
Takufumi Yanagisawa ◽  
Kojiro Matsushita ◽  
Hisato Sugata ◽  
Yukiyasu Kamitani ◽  
...  
Keyword(s):  
Integrative Approach ◽  
Noninvasive Evaluation ◽  
Brain Machine Interface ◽  
Input Devices ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Brain Signals ◽  
Machine Interface ◽  
Robotic Arm Control ◽  
Direct Use

The brain-machine interface (BMI) enables us to control machines and to communicate with others, not with the use of input devices, but through the direct use of brain signals. This chapter describes the integrative approach the authors used to develop a BMI system with brain surface electrodes for real-time robotic arm control in severely disabled people, such as amyotrophic lateral sclerosis patients. This integrative BMI approach includes effective brain signal recording, accurate neural decoding, robust robotic control, a wireless and fully implantable device, and a noninvasive evaluation of surgical indications.

scholarly journals A Fully-Implantable Wireless System for Human Brain-Machine Interfaces Using Brain Surface Electrodes: W-HERBS

2011 ◽  
Vol E94-B (9) ◽  
pp. 2448-2453 ◽  
Author(s):  
Masayuki HIRATA ◽  
Kojiro MATSUSHITA ◽  
Takafumi SUZUKI ◽  
Takeshi YOSHIDA ◽  
Fumihiro SATO ◽  
...  
Keyword(s):  
Human Brain ◽  
Wireless System ◽  
Brain Machine Interfaces ◽  
Surface Electrodes ◽  
Brain Surface

scholarly journals Large-scale meta-analysis suggests low regional modularity in lateral frontal cortex

2016 ◽  
Author(s):  
Alejandro de la Vega ◽  
Tal Yarkoni ◽  
Tor D. Wager ◽  
Marie T. Banich
Keyword(s):  
Frontal Cortex ◽  
Large Scale ◽  
Meta Analysis ◽  
Brain Networks ◽  
Mental States ◽  
Whole Brain ◽  
Psychological States ◽  
Fmri Study ◽  
Data Driven Approach ◽  
Modes Of Processing

AbstractExtensive fMRI study of human lateral frontal cortex (LFC) has yet to yield a consensus mapping between discrete anatomy and psychological states, partly due to the difficulty of inferring mental states in individual studies. Here, we used a data-driven approach to generate a comprehensive functional-anatomical mapping of LFC from 11,406 neuroimaging studies. We identified putatively separable LFC regions on the basis of whole-brain co-activation, revealing 14 clusters organized into three whole-brain networks. Next, we used multivariate classification to identify the psychological states that best predicted activity in each sub-region, resulting in preferential psychological profiles. We observed large functional differences between networks, suggesting brain networks support distinct modes of processing. Within each network, however, we observed low functional specificity, suggesting discrete psychological states are not modularly organized. Our results are consistent with the view that individual LFC regions work as part of highly parallel, distributed networks to give rise to flexible, adaptive behavior.

Motor Restoration Based on the Brain–Machine Interface Using Brain Surface Electrodes: Real-Time Robot Control and a Fully Implantable Wireless System

2012 ◽  
Vol 26 (3-4) ◽  
pp. 399-408 ◽  
Author(s):  
Masayuki Hirata ◽  
Kojiro Matsushita ◽  
Takufumi Yanagisawa ◽  
Tetsu Goto ◽  
Shayne Morris ◽  
...  
Keyword(s):  
Real Time ◽  
Robot Control ◽  
Brain Machine Interface ◽  
Wireless System ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Machine Interface ◽  
The Brain

scholarly journals Brain-Machine Interface using Brain Surface Electrodes : Real-time Robotic Control and a Fully Implantable Wireless System(Development of Neuro-rehabilitation)

2012 ◽  
Vol 21 (7) ◽  
pp. 541-549
Author(s):  
Masayuki Hirata ◽  
Takufumi Yanagisawa ◽  
Kojiro Matsushita ◽  
Morris Shayne ◽  
Yukiyasu Kamitani ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Machine Interface ◽  
Robotic Control ◽  
Special Issue ◽  
Wireless System ◽  
Surface Electrodes ◽  
Brain Surface ◽  
Machine Interface

scholarly journals Effective psychological treatment for PTSD changes the dynamics of specific large-scale brain networks

2020 ◽  
Author(s):  
Marina Charquero-Ballester ◽  
Birgit Kleim ◽  
Diego Vidaurre ◽  
Christian Ruff ◽  
Eloise Stark ◽  
...  
Keyword(s):  
Cognitive Therapy ◽  
Large Scale ◽  
Psychological Treatment ◽  
Brain Activity ◽  
Brain Networks ◽  
Post Traumatic Stress ◽  
Default Mode ◽  
Temporal Precision ◽  
Data Driven Approach ◽  
Post Traumatic

AbstractVery little is known about the role of effective cognitive therapy in reversing imbalances in brain activity after trauma. We hypothesised that exaggerated threat perception characteristic of post-traumatic stress disorder (PTSD), and subsequent recovery from this disorder, are underpinned by changes in the dynamics of large-scale brain networks. Here, we use a novel data-driven approach with high temporal precision to find recurring brain networks from fMRI data and estimate when these networks become active during exposure to either trauma reminders or neutral pictures. We found that PTSD patients spend less time in two default mode sub-networks in contrast to trauma-exposed healthy controls, and that PTSD symptom severity correlates positively with time spent in the salience network during exposure to trauma reminders. The former are important for different aspects of self-referential processing and the latter for detection of threat. Importantly, the decreased time in the default mode sub-networks is rebalanced after successful cognitive therapy for PTSD. Our results show that remittance of PTSD through trauma-focused cognitive therapy is associated with the successful reinstatement of a healthy balance in self-referential and threat detection brain networks.

Cortico-Cortical Evoked Potential Mapping

2018 ◽  
pp. 431-452 ◽  
Author(s):  
Riki Matsumoto ◽  
Takeharu Kunieda
Keyword(s):  
Human Brain ◽  
Cortical Excitability ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Single Pulse ◽  
Functional Brain ◽  
Cortical Connections ◽  
Functional Brain Networks ◽  
Cortical Regions

The utility of single-pulse electrical stimulation (SPES) for epilepsy surgery has been highlighted in the last decade. When applied at a frequency of about 1 Hz, it can probe cortico-cortical connections by averaging electrocorticographic signal time-locked to stimuli to record cortico-cortical evoked potentials (CCEPs) emanating from adjacent and remote cortices. Although limited to patients undergoing invasive presurgical evaluations, CCEPs provide a novel way to explore inter-regional connectivity in vivo in the living human brain to probe functional brain networks such as language and cognitive motor networks. In addition to its impact on basic systems neuroscience, this method, in combination with 50 Hz electrical cortical stimulation, can contribute clinically to the mapping of functional brain systems by tracking cortico-cortical connections among functional cortical regions in individual patients. This approach may help identify normal cortico-cortical networks in pathological brain, or plasticity of brain systems in conjunction with pathology. Because of its high practical value, it has been applied to intraoperative monitoring of functional brain networks in patients with brain tumours. With regard to epilepsy, SPES has been used to probe cortical excitability of the focus (epileptogenicity) and seizure networks. Both early (i.e. CCEP) and delayed responses are regarded as surrogate markers of epileptogenicity. With regard to its potential impact on human brain connectivity maps, worldwide collaboration is warranted to establish standardized CCEP connectivity maps as a solid reference for non-invasive connectome research.

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